CA2595568C - Edge based cmy automatic picture registration - Google Patents

Abstract

An automatic process for performing CMY (Cyan, Magenta, Yellow) registration for film digital restoration. After three color components Cyan, Magenta, and Yellow of a picture are scanned into files, each component is divided into blocks (18), and edge detection (16) is applied to each block, and an edge match (20) is performed. The data of displacements is processed (22), and then affine transform parameters are calculated (24). The affine transform is then applied for each block (26), and warping is used to combine the color components (28) and obtain the registered picture of a color component.

Description

.PU050018 EDGE BASED CMY AUTOMATIC PICTURE REGISTRATION
BACKGROUND OF THE INVENTION
1. Field of the Invention The present invention relates to film preservation and restorations. More particularly, it relates to a method for automatically registering the color components (Cyan, Magenta, Yellow) of a color film for use in preservation and restoration applications.

2. Description of the prior art In order to store a color film for a long time and reduce the effect of color fading, the color film is separated into three-color components, Cyan, Magenta, and Yellow (CMY). Each color component is stored on a separate reel. When the time comes to re-release the film, the color components on each of these three reels need to be re-combined. In this regard, the CMY components need to be registered to obtain resulting re-combined color images that appear to be the same color as the images on the original color film. Most CMY registration is performed using photo-chemical techniques. Unfortunately, as the CMY reels age, the film on each reel is subject to distortion or shrinkage. In this environment, such photo-chemical based CMY
registration does not perform well. As such, it requires registration to be performed using digital technology. In this case, registration is performed manually.
However, manual registration is labor and cost intensive.

SUMMARY OF THE INVENTION
In accordance with the principles of the invention, a digital image registration technique automatically performs registration. In addition, the digital image registration technique can also register severely distorted color components very accurately by warping images.
According to one embodiment, the method for automatically registering the color components of a color film includes determining correlations between the color components of the color film, processing the correlation data, determining Affine Transform parameters for the color components, calculating the Affine Transform for each pixel in the respective color component using the determined parameters, and combining the color components to re-produce the color film.
In order to determine the color component correlations, a base color is selected and initial displacement coefficient vector values of the other color components with respect to the selected base color component is calculated. If necessary, each picture frame can be divided into blocks. The picture frames are edge detected for the respective color components, and the detected edges are matched for each color component with respect to the base color component.
Once complete, new displacement vector values are calculated using the initially calculated displacement coefficient vector values.
According to a further embodiment, the method includes an error correction aspect to the correlation processing stage. Initially, a determination is made whether any large errors are present in the determined correlations. Any large errors are modified and new displacement value coefficients are calculated. The displacement vector values are then re-calculated using the newly calculated displacement value coefficients. In order to calculate the new displacement value coefficients, a 3-order curve or 3-order plane is applied to best fit the error numbers in either direction.
The modifying of errors can be performed using by using neighboring block values or interpolation/extrapolation.
In order to calculate the Affine transform includes determining pixel position in the original picture for each pixel in a block using a nearest block analysis, and defining affine transform parameters using the determined pixel positions and displacement values of the corresponding nearest blocks.
According to another embodiment, the combining is performed by warping non base color components with the base color component to form the registered color image. The warping includes mapping each pixel in the new picture onto the old picture using the calculated Affine Transform.
In yet another embodiment, the method for edge based CMY automatic picture registration of a color film includes determining displacement values between a base color component and other color components of a color film, and processing the correlation data to obtain new displacement value coefficients corresponding to the determined displacement values identify and remove errors. Once processing is complete, Affine Transform parameters are determined for the other color components, and the Affine Transform for each pixel in the respective color component is calculated using the determined parameters. The color components are then combined to re-produce the color film.
Other aspects and features of the present invention will become apparent from the following detailed description considered in conjunction with the accompanying drawings. It is to be understood, however, that the drawings are designed solely for purposes of illustration and not as a definition of the limits of the invention, for which reference should be made to the appended claims.
It should be further understood that the drawings are not necessarily drawn to scale and that, unless otherwise indicated, they are merely intended to conceptually illustrate the structures and procedures described herein.

BRIEF DESCRIPTION OF THE DRAWINGS
In the drawings wherein like reference numerals denote similar components throughout the views:
Figure 1 is block diagram of the automatic color combination algorithm according to an embodiment of the invention;
Figure 2 is an example of a block division according to one embodiment of the invention;
Figure 3 is another example of a block division according to another embodiment of the invention;
Figure 4 is an exemplary diagram of the edge matching method according to an embodiment of the invention;
Figures 5a-5f are a further example of the block division and related affine transform displacement value application according to an embodiment of the invention;
Figure 6a ¨ 6c are exemplary data sets used to represent the calculation of the affine transform according to an embodiment of the invention; and Figure 7 is a diagrammatic representation of the method for obtaining the warping component picture using the calculated Affine Transfolin according to an embodiment of the invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
The concepts of the present invention utilize known elements and processes in the film processing art. For example, the details of film processing, affine transformations, CMY
registration, etc., are well known and not described in detail herein. In addition, the invention may be implemented using conventional programming techniques.
In a preferred embodiment, the invention is implemented in software. This invention can be, but is not limited to being, embedded in firmware, resident on microcomputer, microcode, etc. Other embodiments may be entirely hardware, entirely software, or a combination of hardware and software elements.
Additionally, the present invention can be in the form of a software product stored or accessible from any computer usable medium providing computer program code.
This includes, but is not limited to any apparatus that may store, communicate, or propagate the program for use by or in connection with, any device capable of program execution. The medium may be optical, electronic, magnetic, electromagnetic, a transmission medium, or a semiconductor medium. A computer readable media may be embodied as a computer hard drive, removable computer disk, random access memory, read only memory, semiconductor or solid state memory device, magnetic tape, punch card, or optical disk.
Current examples of optical disks include Compact Discs (CDs), Digital Video Discs (DVDs), High Definition DVDs (HD-DVDs), LaserdiscTM, Blu-RayTM Discs, MinidiscTM, or magneto-optical discs.
With the exception of Laserdiscs, all of these disks may be in a fixed read only memory (ROM), recordable ( R), or recordable/rewriteable (-RW) format.
A data processing system may be comprised of one or more processors with supporting electronic devices such as a motherboard. These processors may include memory resident on the processor or connected via a system bus to local memory, cache memory, or shared system or network memory. The data processing system may be coupled to input devices such as keyboards or mice, output devices such as displays or printers, and communications adapters such as network cards, modems, or networking backplanes.
Network adapters that may be included in a data processing system allow data to be transferred across an intervening public or private network to and from other terminals, servers, printers, or remote storage devices. Some current examples of network adapters are Ethernet adapters, wireless WiFiTM and WiMaxTm adapters, token ring adapters, etc. Current networks include Local Area Networks (LANs), Wide Area Networks (WANs), the Internet, ad hoc networks, direct connect networks or virtual private networks (VPNs).

In accordance with the principles of the invention, the image registration process automatically performs registration for the Cyan, Magenta, and Yellow (CMY) color components in the digital domain. Those of ordinary skill in the art will recognize that concepts disclosed herein are not limited to C, M, and Y, and may be use for other color spaces as well, or between any two color components.
A 3-reel CMY film is scanned into three mono sequences. The picture size can be 2K or 4K (one K is 1024 bytes). In the film industry, the resolution of 2K is 2048 x 1556 pixels and the resolution of 4K is 4096 x 3112 pixels. The bit-depth of a pixel is irrelevant to the present invention and is typically 10 bits. The scanned files are illustratively stored in a dpx format (SMPTE (Society of Motion Picture and Television Engineers) Digital Picture Exchange Format). However, other file formats can be used and supported without departing from the invention described herein.
The registration process of the present invention operates on one frame of image data at a time. As described below, there may be circumstances that make it necessary to further divide the frame of image date into blocks and, if possible, sub blocks or sub pictures in order to continue the processing.
One factor that may require dividing the picture into blocks or sub blocks can be distortion of the source image data. Depending on the severity of distortion, the picture may require division into blocks (i.e., when the non-linearity of the distortion cannot be ignored).
A block can have some overlap with its adjacent blocks or it can have no overlap at all. The number of blocks is determined based on the contents of the picture data, which number can be a very rough estimate before actually performing the block division.
Generally, increasing the accuracy of the registration process requires more blocks.
However, increasing the number of blocks means each block will be smaller in size, and the smaller block size, the potential for lower accuracy in the calculated displacement accuracy is higher (i.e., if the block is too small, there may not be enough information, thus lower accuracy).
According the principles of the present invention very small blocks are not required to perform the automatic picture registration. Experimental results indicate that the number of blocks for 2K materials can be lx1 , 2x2, 2x4, 4x4, 4x6, or 4x8, to just name a few. Although possible, no more than 8 blocks in either dimension are should be required.
In order to register three (3) color components (e.g., CMY, RGB), the correlations between them need to be determined. There are several ways to calculate these correlations.
Illustratively, edge correlation or edge matching is used. In this regard, there are two steps: edge detection and edge matching. Any existing edge detection techniques can be used, for example the Canny edge detection technique as known in the art. The edge matching is implemented after the edges are detected. Any of the three colors components to be registered can be chosen as a base, and displacements (i.e., correlations) of the other two color components can be calculated from the chosen base component.
By way of example, there are two displacement vectors for each block, (Vxrg, VYrg), (Vxrb, VYrb), where Vrg is a displacement between red and green in the x direction, and VYrg is a displacement between red and green in the y direction. Similarly, Vxrb and VYrb are displacements between red and blue in the x and y directions, respectively.
Here we assume the red component is used as the base.
In order to assist in the correlation determination, it is preferred to divide each frame into blocks. When the picture is divided into blocks, all the image data is better configured to be processed to eliminate big errors and to make the values of the displacements change smoothly across the picture. For example, a one-dimensional 3-order curve in either the x or y direction can be used, or a two-dimensional 3-order plane. Also, a lower order or a higher order curve or plane can be used. When the number of blocks in a direction is less than three, then no curve fit is taken for the displacement values.
The adjusted values of displacement (parameters) are used to calculate six parameters of an affine transform. Four displacement vectors are used to calculate the affine transform of each block, and redundancy can be used to reduce errors. However, the present invention does not require the use of redundancy to reduce errors, but may use it to find a pixel in a new picture to its corresponding location in the old picture, and obtain the pixel value by interpolating. The new picture is saved in a file format, such as dpx, yuv, raw, or ppm as known in the art.
An illustration of the automatic color combination process 10 is shown in Figure la. The inputs 12a, 12b, and 12c are three separate color components.
These color components can be stored in a single file or separate files. In a preferred embodiment, the inputs are three separate DPX files, one for each color component.
There are potentially two options in the next step 14: 1) Edges are detected first by an edge detector 16 and then the edge picture is divided into blocks using a divider 18 (See Figure la); or 2) A picture can be divided into blocks first using a divider 18 and then implement edge detection 16 (See Figure lb). The resulting outputs for these two methods however, may be different. Step 14 is the first step in the correlation determination of the present invention.

Dividers 18 utilize two parameters to divide the picture, 1) the number of blocks in the horizontal direction, and 2) the number of blocks in the vertical direction. As mentioned above, the blocks can be overlapping or non-overlapping, where the portion of overlap can be varying, and the block size can be different from one to another. If the block size is varying, the size is determined by the contents of the picture. The rich texture areas of the picture can have small blocks, and the less texture areas can have big blocks.
Figure 2 shows an embodiment of four (4) overlapping blocks of fixed size.
Note, area e is where two adjacent blocks overlap, and area f is where all four blocks overlap.
Figure 3 shows an embodiment of four (4) non-overlapping blocks that vary in size.
As noted above, for performing edge detection, any existing edge detector can be used, such as, e.g., the above-mentioned Canny edge detector. All the edges are a single pixel thick for Canny edge detection. Other edge detectors may have multi-pixel thick edges.
As the final part of the correlation determination process, the divided/ edge detected images are edge matched 20. For edge matching 20, a search window is opened on the base color edge picture. For each position in the search window, a non-base color edge block is compared with the base color. The number of unmatched edge points is calculated, and the smallest number is picked as the best match; or alternatively, the number of matched edge points is calculated and the largest number is picked as the best match.
According to other embodiments, the best match may be tested to avoid a mis-pick. One example of such a test is now described with reference to Figure 4.
The number of the mismatch edge points at the position a should be less than the number of any of the eight (8) positions of b and d. A loose test is where the mismatch number at the position a should be less than any number at the four (4) positions of d. Those of skill in the art will recognize that that the data set may be low pass filtered first to obtain minimums or maximums, or simply to improve accuracy.
Once the edge matching is performed, additional data processing 22 of the image is required for the registration process. This data processing provides an error correction/prevention stage, and further improves accuracy by using the newly calculated displacement values (vectors). An illustrative technique for data processing 22 according with the principles of the invention is as follows. For each block, there are 2 displacement vectors (i.e., x and y). Each displacement vector represents a displacement between a color component edge map to the base color component edge map in the horizontal and vertical directions. For a picture of in by 7Z blocks with a fixed block size, there are four sets of data:

V11,iy, Vii2x, where ij are the indices of a block, and m, n are the number of blocks in x and y directions, respectively.
Here 1õ and \Tilly are used as examples to show how to process the data. It is illustratively assumed that in = 5, a = 5, and V'3 lx is a 5x5 matrix.
Step 1: Use a pre-set threshold to find out if there are any big error numbers.
Step 2: Modify the big error numbers by using their neighbor values or by interpolation/extrapolation.
Step 3: Use 3-order curves to best fit the numbers in either direction or use a 3-order plane. For a 3-order curve:
f(x) = ao + ai*x + a2*x2 + a3*x3, and (1) f(Y) = bo + bi*y + b2*y2 + b3*y3 (2) and for a 3-order plane:
f(x, y) =
ao + al*x + a2*y +a x +a v + a x v+ 2 x V + X
V+ a X V (1) __3*. _4*, 2 _.6*. . 3 . *, . *
where a1, and bi are coefficients of the polynomial curve or plane. Certainly, lower order or higher order can be used.
If 3-order curve is used, for each row of a matrix, [xo, xl, x2, x3, x4], the corresponding polynomial coefficients are calculated as follows:
X=[1 xo x02 x03 ; 1 xi x12 x13 ; 1 x9 x22 x23 ; 1 x3 x32 x33 ; 1 x4 x42 x43], (4) where X is a 5x4 matrix, the semicolon µ;' is a row separator and xi is the position of the corresponding block i in x direction in the row.
F=[f(xo) f(xi) f(x2) f(x3) f(x4)i, (5) where F is a vector and f(xi) is the displacement of the corresponding block i in the row.
A=[ ao al a2 a3], (6) where A is coefficient vector and is initially unknown.
Then, F=X*A, and (7) A=0(Tx)-IxTF, (8) where XTX is positive definition, and it is inversible.
Step 4: Re-calculate the displacement values of F by using the coefficients A:
F'=X*A; (9) where F' is a new data set that is used to replace the old one. After all rows are processed, a = new matrix is created with all the new numbers of F's. The parameters at most outside positions may be further modified, such that their values are within a certain range of the values of the second most outside parameters of the matrix.
A 3-order plane can be calculated in a similar fashion, except the matrix is bigger. For example, matrix Xis 25x10, F is 10x1, and A is 10x1.
Once the data processing is complete, the Affine Transform for each color component block needs to be calculated 24 (See Figure 1). For each color component, there are two data sets, one in the x-direction and the other in the y-direction.
For each pixel position of a block, a nearest block analysis is used to determine the pixel position in the original picture. The present invention is described using the four nearest neighbor blocks to make this determination. However, those of skill in the art will recognize that the number of blocks used in a nearest block analysis is a matter of choice and can be higher or lower than the "four nearest neighbor" example described herein.
Figures 5a-f show an example of this concept. A more general case is shown in Figure 5a where if a block (I) has eight neighbor blocks, sub block 11 will use the displacement values of blocks A, B , D, I to determine the parameters of the affine transformation. It follows that sub block 12 will use the displacement values of blocks B, C, E, and I, sub block 21 will use the displacement values of blocks D, F, I, G, and sub block 22 will use the displacement values of blocks E, H, G, and I. For other cases, if block I is located at a side or a corner of a picture (FIGS. 5e & 5f), then the respective sub block 11, 12, 21, 22 will use its nearest three neighboring blocks and block I to calculate the affine transform parameters.
Figures 6a, 6b and 6c indicate the new pixel (x, y) in the middle of 4 block centers (FIG. 6a), at a picture corner (FIG. 6b), and at one side of the 4 block centers (FIG.
6c), respectively.
The affine transform is shown below, _ ao bo c0-1 x new ¨ X old I
I 1Y wit, = (10) _y õidIj= _al c1]

The positions of the 4 old points (block centers) are known (shown in FIG. 6), and the positions of the 4 points in the new picture can be obtained by adding the displacements to the corresponding points. There are 8 equations and six unknowns, as such, the 6 parameter affine transform can be easily obtained. In some cases when more than two equations are linear combinations of the other equations, the affine transform is reduced to:

]
Xoldl ao 0 co X new I Y new = 0 b, cli (11) For warping of a picture (step 26 Figure 1), warping is applied to two of the three color components. More generally, if there are N components, warping will be applied to N-1 color components. Those of skill in the art will recognize that image warping is one kind of image transformations, which can be linear or non-linear transformation. The one color component that is used for the base will not be warped (in the exemplary case of Figure 1, M/G). The warping component picture is obtained by using the calculated affine transform to map each pixel in the new picture onto the old picture (illustrated in FIG.
7). This is done by combining (28) the two warping color components with the base color component to form the registered color image. The mapped pixel (rn,n) in the old picture is normally not on the integer grid, however, the value of the pixel can be obtained by using interpolation or the nearest pixel value. Any of several known interpolation techniques, such as bi-linear, bi-cubical, etc. may be used for this purpose. After warping, the three color components can be converted to certain desired file or image formats, and form a registered color image.
The above-described automatic registration process has been tested on a number of films with good results.
In order to speed up the registration process, the picture can be down sampled to a lower resolution, for example from 4K down sampled to 2K, and the best matched displacement vectors of each sub picture can be computed at the lower resolution. Then the matched displacement vectors are up-scaled to the original resolution, these vectors are used to perform the picture warping at original resolution, 4K.
In another case, in order to reduce the scanning cost, lower resolution is used.
Thus, the lower scanned resolution, the lower the cost. The magenta channel can be scanned at the high resolution, for example 4K, and cyan and yellow channels can be scanned at lower resolution, for example 2K. The magenta channel is the most dominant of the three channels, thus enabling this different resolution approach to the less dominant cyan and yellow channels. This process effectively up-scales the cyan and yellow channels to the resolution of the magenta channel. Then the registration can be done in high resolution.
It should also be noted that the above-described registration process can occur at the original separation of a color film into the CMY components as a check on the quality of the separation, e.g., to check if a component of a picture is missing or is damaged, etc.

PU050018 ' In view of the above, the foregoing merely illustrates the principles of the invention and it will thus be appreciated that those skilled in the art will be able to devise numerous alternative arrangements which, although not explicitly described herein, embody the principles of the invention described herein.

Claims (10)

WHAT IS CLAIMED IS:

1. A method for automatically registering color components of a color film, the method comprising the steps of:
downsampling, from a desired resolution to a lower resolution, image data of the color components of the color film to be registered to speed up processing;
determining correlation data between the color components of the color film;
processing the correlation data;
upscaling matched displacement vectors to the desired resolution before combining is performed;
determining Affine Transform parameters for the color components;
calculating the Affine Transform for each pixel in the respective color component using the determined parameters; and combining the color components to re-produce the color film.

2. The method according to claim 1, wherein said determining correlations comprises:
selecting a base color component;
calculating initial displacement coefficient vector values of the other color components with respect to said selected base color component;
dividing each picture frame into blocks;
detecting picture frame edges for the color components; and matching the detected edges of each color component with respect to the selected base color component.

3. The method according to claim 2, wherein said processing of the determined correlations comprises calculating new displacement vector values using the initially calculated displacement coefficient vector values.

4. The method according to claim 2, wherein said processing of the determined correlations comprises:
determining whether any large errors are present in the determined correlations;
modifying any large errors;
calculating new displacement value coefficients; and re-calculating the displacement vector values using the newly calculated displacement value coefficients.

5. The method according to claim 4, wherein said determining is performed using a predetermined threshold.

6. The method according to claim 4, wherein said modifying errors is performed using by using neighboring block values or interpolation/extrapolation.

7. The method according to claim 4, wherein said calculating new displacement value coefficients is performed by applying a 3-order curve or 3-order plane to best fit error numbers in either direction.

8. The method according to claim 2, wherein said calculating the Affine transform comprises:
determining pixel position in the original picture for each pixel in a block using a nearest block analysis; and defining affine transform parameters using the determined pixel positions and displacement values of the corresponding nearest blocks.

9. The method according to claim 1, wherein said combining comprises warping non base color components with the base color component to form a registered color image.

10. The method according to claim 9, further comprising converting the color components to a desired file or image format before forming the registered color image.